How to ensure consistency in SIFT features?

I am working with a classification algorithm that requires the size of the feature vector of all samples in training and testing to be the same.

I am also to use the SIFT feature extractor. This is causing problems as the feature vector of every image is coming up as a different sized matrix. I know that SIFT detects variable keypoints in each image, but is there a way to ensure that the size of the SIFT features is consistent so that I do not get a dimension mismatch error.

I have tried rootSIFT as a workaround:

[~, features] = vl_sift(single(images{i}));
        double_features = double(features);
        root_it = sqrt( double_features/sum(double_features) ); %root-sift
        feats{i} = root_it;

This gives me a consistent 128 x 1 vector for every image, but it is not working for me as the size of each vector is now very small and I am getting a lot of NaN in my classification result.

Is there any way to solve this?